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Digging Numbers

These exercises are based on the textbook “Digging Numbers” by Mike Fletcher and Gary Lock (ISBN 978-0947816698). This book is an introduction to statistics written for archaeologists by archaeologists. It consists of four parts, the third being about practical examples of application using known commercial statistical software. These pages are meant to provide a “translation” of the same exercises using free software.

Please bear in mind that this tutorial is not a substitute for a complete book of quantitative archaeology or statistics. Currently it's going to be hard to follow the tutorials without having Digging Numbers with you. Maybe in the future we will add more content to this website, but at the moment it's quite useless if taken alone.

Our first software choice is the R statistical suite.

R (http://www.r-project.org/) runs on a variety of platforms, including GNU/Linux, MacOS X, Microsoft Windows and others. Installation should be straight-forward in most cases. On some platforms, R is provided with a basic GUI, but we will be using the command line, because it is more powerful and gives access to all the available options. Moreover, the command line interface is the same on each platform. Our examples are based on a GNU/Linux system, because we really like free software

Anytime you need to look at the documentation, you can use the help(command.name) function inside the R environment, which gives you a general overview of the command you asked for, the available parameters, bibliographic references and examples. Here's an example:

> help(summary)

Syntax

These pages deal with entering commands typing with your keyboard. When you start R, you get the so-called prompt. It's R's way to say that it's ready to accept your commands.

The standard R prompt looks like this:

>

What you type goes after the > symbol:

> ls()

From now on, text that comes formatted like above means “this is a command”. We won't be omitting the initial >, so if you wish to copy & paste commands from this tutorial straight into the R window, you need to avoid highlighting the “>”. In general however, it is best if you type out the commands. This is not as bad as it sounds and will actually help you learn the program commands.

Data

The authors of the book included a dataset that contains various measurements and data about 40 spearheads from English archaeological sites. The example data used here are the same used in Digging Numbers itself, and by kind permission of Professor Gary Lock we can now provide the complete dataset in XLS or CSV format. CSV is best suited for importing the data straight into R.